The invention relates to methods for evaluation a brightness level of the digital x-ray image for medical applications by means of the image histogram using a neural network. The calculations comprise of: image acquisition, image histogram calculation, transformation the frequencies of the histogram into input arguments of the neural network and calculation the brightness level as linear transform of the output value of the neural network. Training of the neural network is performed over a learning set calculated over the given image database. The transformed frequencies of histograms of these images are used as a set of input arguments of the neural network. The brightness levels calculated for each image over the region of interest and scaled to the range of output values of the neuron network are used as a set of target values.
Legal claims defining the scope of protection, as filed with the USPTO.
1. A method for determining brightness of a medical digital X-ray image comprising: creating an image database; training a feedforward neural network with one hidden layer and sigmoid functions of neurons; using transformed frequencies of image histograms as input arguments for the neural network; using brightness values calculated over a region of interest and scaled to an output value range of the neuron network as target values for the neural network training; acquisition of a current medical digital X-ray image; calculation of a histogram of the current image; transformation of histogram frequencies into input arguments for the neuron network; and calculation of the brightness as a linear function of an output value of the neural network.
2. The method according to claim 1 , wherein, for calculation of the target values for the neural network training, the brightness of an image in the image database is calculated as a mean brightness of pixels within the region of interest.
3. The method according to claim 1 , wherein the histogram is calculated over all pixels of the current image.
4. The method according to claim 1 , wherein the histogram is calculated over pixels within a circle centered at a center of the current image and having a diameter equal to a shortest dimension of the current image.
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March 26, 2013
October 28, 2014
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